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Parallel Python
Interactive parallel computing
Research Computing @ CU Boulder
Monday, April 23, 12
Why Python?
• Relatively easy to learn
• Interactive
• Cross platform
• Linux
• Windows
• OS/X
• Large number of packages
• Numpy – for numerical computation
• Matplotlib - plotting
• Ipython – interactive shell
Research Computing @ CU Boulder
Monday, April 23, 12
IPython
• Supports different styles of parallelism
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Single program, multiple data (SPMD) parallelism
Multiple program, multiple data (MPMD) parallelism
Message passing using MPI
Task farming
Combinations of above approaches
Custom user defined approaches
• Interactive approach to
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Development
Execution
Debugging
Monitoring
Research Computing @ CU Boulder
Monday, April 23, 12
Overview
• How it works
• Direct interface
• Executing remote commands
• Data management
• Blocking, non-blocking
• Task interface
• Compare with pbsdsh
• Simple, robust, and load-balanced
Research Computing @ CU Boulder
Monday, April 23, 12
How it works
Research Computing @ CU Boulder
Monday, April 23, 12
Architecture
• Engine
• Takes Python commands over a network connection
• One engine per core
• Blocks code: controller has an asynchronous API
• Controller client
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Interface for working with a set of engines
Hub
Collection of schedulers
View objects represent a subset of engines
• A Direct interface, where engines are addressed explicitly.
• A LoadBalanced interface, where the Scheduler is trusted
with assigning work to appropriate engines.
Research Computing @ CU Boulder
Monday, April 23, 12
Parallel Architecture
• Hub
• keeps track of engine
connections, schedulers,
clients
• Cluster state
• Scheduler
• All actions that can be
performed on the engine go
through a scheduler
Research Computing @ CU Boulder
Monday, April 23, 12
Start your Engines
• Simplest method
• ipcluster start --n=4
• MPIEXEC/MPIRUN mode
• SSH mode
• PBS mode for batch processing
• Other methods
• Ipcontroller
• ipengine
• Don’t forget to turn off your engine!
Research Computing @ CU Boulder
Monday, April 23, 12
JANUS
• use .epd-7.1-2
• use .openmpi-1.4.3_gcc-4.5.2_torque-2.5.8_ib
• ipython profile create --parallel --profile=mpi
Genera&ng default config file: u'/home/molu8455/.ipython/profile_mpi/ipython_config.py'
Genera&ng default config file: u'/home/molu8455/.ipython/profile_mpi/ipython_qtconsole_config.py'
Genera&ng default config file: u'/home/molu8455/.ipython/profile_mpi/ipcontroller_config.py'
Genera&ng default config file: u'/home/molu8455/.ipython/profile_mpi/ipengine_config.py'
Genera&ng default config file: u'/home/molu8455/.ipython/profile_mpi/ipcluster_config.py'
Genera&ng default config file: u'/home/molu8455/.ipython/profile_mpi/iplogger_config.py'
• Edit the file ipcluster_config.py
• c.IPClusterEngines.engine_launcher_class =
'MPIEngineSetLauncher'
Research Computing @ CU Boulder
Monday, April 23, 12
Steps
• Request a resource with qsub:
• Interactive
• Batch
• Start the engines
• ipcluster start --profile=mpi &
• Parallel python work ...
• Stop the engines
• ipcluster stop --profile=mpi
Research Computing @ CU Boulder
Monday, April 23, 12
Example
#PBS -N example
#PBS -j oe
#PBS -l walltime=00:45:00
#PBS -l nodes=2:ppn=12
#PBS -q janus-debug
. /curc/tools/utils/dkinit
reuse .epd-7.1-2
reuse .openmpi-1.4.3_intel-12.0_ib
cd $PBS_O_WORKDIR
ipcluster start --profile=mpi &
sleep 90
python work.py
ipcluster stop --profile=mpi
Research Computing @ CU Boulder
Monday, April 23, 12
A few simple concepts
• The client: lightweight handle on all engines of a cluster
from IPython.parallel import Client
rc = Client(profile='mpi')
• The views: “slice” the client with specific execution
semantics
• DirectView: direct execution on all engines
dview = rc.direct_view()
• LoadBalancedView: run on any one engine
lview = rc.load_balanced_view()
Research Computing @ CU Boulder
Monday, April 23, 12
Direct Interface
Research Computing @ CU Boulder
Monday, April 23, 12
Direct Interface
• capabilities of each engine are directly and explicitly
exposed to the user
• Blocking and non-blocking
• AsyncResult
• Remote execution
• apply
• execute
• map
• Remote function decorators
• Managing data
• push, pull
• scatter, gather
Research Computing @ CU Boulder
Monday, April 23, 12
Blocking vs. Non-blocking
• Blocking
• waits until all engines are done executing the command
dview.block=True
• Non-blocking
• returns an AsyncResult immediately
• Get data later with get method
• Check the status with ready
• Wait
dview.block=False
Research Computing @ CU Boulder
Monday, April 23, 12
Apply Example
• Serial
• Parallel
import os
pid = os.getpid()
print pid
with dview.sync_imports():
import os
dview.block=True
pids = dview.apply(os.getpid)
dview.block=False
ar = dview.apply(os.getpid)
dview.wait(ar)
pids = ar.get()
Research Computing @ CU Boulder
Monday, April 23, 12
Apply
• For convenience
• apply_sync
(block=True)
• apply_async
(block=False)
def wait(t):
import time
tic = time.time()
time.sleep(t)
return time.time()-tic
ar = dview.apply_async(wait,1)
if ar.ready() == True:
print "ready"
else:
print "not-ready"
dview.wait(ar)
Research Computing @ CU Boulder
Monday, April 23, 12
Execute
• Commands can be executed as strings on specific
engines
dview['a'] = 10
dview['b'] = 20
dview.execute('c=a*b')
rc[1].execute('c=a+b')
print dview['c']
[200, 30, 200, 200, 200, 200, 200, 200, 200, 200, 200, 200]
Research Computing @ CU Boulder
Monday, April 23, 12
Map
• Applies a sequence of to a function element-by-element
• Does not do dynamic load balancing
• map_sync, map_async
def fsquare(x):
return x**2
xvals = range(32)
s_result = map(fsquare, xvals
dview.block=True
p_result = dview.map(fsquare, xvals)
Research Computing @ CU Boulder
Monday, April 23, 12
Push and Pull
• Limited to dictionary types
dview.block=True
values = dict(a=1.03234,b=3453)
dview.push(values)
a_values = dview.pull('a')
print a_values
dview.push(dict(c='speed'))
print dview.pull('c')
dview['c'] = 'speed'
print dview.pull('c')
Research Computing @ CU Boulder
Monday, April 23, 12
Scatter and Gather
• Partition a sequence and push/pull
dview.scatter('a',range(24))
print dview['a']
all_a = dview.gather('a')
print all_a
# Use
dview.scatter('x',range(64))
dview.execute('y=map(fsquare, x)')
y = dview.gather('y')
print y
Research Computing @ CU Boulder
Monday, April 23, 12
Task Interface
Research Computing @ CU Boulder
Monday, April 23, 12
Task interface
• Presents the engines as a fault tolerant, dynamic loadbalanced system of workers
• No direct access to individual engines
• Simple and powerful
lview = rc.load_balanced_view()
• Components:
• map
• Parallel function decoders
• Dependencies
• functional
• graph
Research Computing @ CU Boulder
Monday, April 23, 12
Example: parameter study
#!/bin/bash
#PBS -N example_mn_1
#PBS -q janus-debug
#PBS -l walltime=0:01:30
#PBS -l nodes=2:ppn=12
cd $PBS_O_WORKDIR
pbsdsh $PBS_O_WORKDIR/
wrapper.sh 0
Research Computing @ CU Boulder
Monday, April 23, 12
Example: parameter study
#!/bin/bash
#PBS -N example_mn_1
#PBS -q janus-debug
#PBS -l walltime=0:01:30
#PBS -l nodes=2:ppn=12
cd $PBS_O_WORKDIR
pbsdsh $PBS_O_WORKDIR/
wrapper.sh 0
#!/bin/bash
PATH=$PBS_O_WORKDIR:$PBS_O_PATH
TRIAL=$(($PBS_VNODENUM + $1))
simulator $TRIAL > $PBS_O_WORKDIR/sim.$TRIAL
Research Computing @ CU Boulder
Monday, April 23, 12
#!/bin/bash
#PBS -N example_mn_3
#PBS -q janus-debug
#PBS -l walltime=0:01:30
#PBS -l nodes=2:ppn=12
cd $PBS_O_WORKDIR
count=0
np=`wc -l < $PBS_NODEFILE`
for i in {1..5}
do
pbsdsh $PBS_O_WORKDIR/
wrapper.sh $count
count=$(( $count + $np))
done
Research Computing @ CU Boulder
Monday, April 23, 12
Problems
• Core failure to run
• Load-balance
Research Computing @ CU Boulder
Monday, April 23, 12
Weaknesses of batch
time
Monday, April 23, 12
time
Load balancing
time
Monday, April 23, 12
time
simulator.py
def simulator(x):
import subprocess
cmd = "./simulator " + str(x) + " 2 4 > trial." + str(x)
p = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE)
(err,out)=p.communicate()
status = p.returncode
return "%s, %s" % (out,err)
print simulator(1)
from IPython.parallel import Client
rc = Client(profile='mpi')
lview = rc.load_balanced_view()
r = lview.map_async(simulator, range(100))
print r.get()
Research Computing @ CU Boulder
Monday, April 23, 12
#!/bin/bash
#PBS -N example_mn_3
#PBS -q janus-debug
#PBS -l walltime=0:01:30
#PBS -l nodes=2:ppn=12
cd $PBS_O_WORKDIR
ipcluster start --profile=mpi &
sleep 90
python simulator.py
ipcluster stop --profile=mpi
Research Computing @ CU Boulder
Monday, April 23, 12
Interactive MPI
• Create MPI functions
def psum(a):
s = np.sum(a)
rcvBuf = np.array(0.0,'d')
MPI.COMM_WORLD.Allreduce([s, MPI.DOUBLE],
[rcvBuf, MPI.DOUBLE],
op=MPI.SUM)
return rcvBuf
• Call interactively from direct view
• Not currently available on JANUS as a dotkit
Research Computing @ CU Boulder
Monday, April 23, 12
Conclusions
• Direct interface
• Quickly parallelize python code
• Less control that MPI
• Task interface
• Fault-tolerant, load-balanced, simple
• MPI interactive
• Great for developing, debugging
• Tutorials:
• http://ipython.org/ipython-doc/dev
• http://minrk.github.com/scipy-tutorial-2011
Research Computing @ CU Boulder
Monday, April 23, 12